Load scripts: loads libraries and useful scripts used in the analyses; all .R files contained in scripts at the root of the factory are automatically loaded
Load data: imports datasets, and may contain some ad hoc changes to the data such as specific data cleaning (not used in other reports), new variables used in the analyses, etc.
library(reportfactory)
library(here)
library(rio)
library(tidyverse)
library(incidence)
library(distcrete)
library(epitrix)
library(earlyR)
library(projections)
library(linelist)
library(remotes)
library(janitor)
library(kableExtra)
library(DT)
library(cyphr)
library(chngpt)
library(lubridate)
library(ggpubr)
library(ggnewscale)These scripts will load:
.R files inside /scripts/.R files inside /src/These scripts also contain routines to access the latest clean encrypted data (see next section).
We import the latest NHS pathways data:
x <- import_pathways() %>%
as_tibble()
x
## [90m# A tibble: 428,473 x 11[39m
## site_type date sex age ccg_code ccg_name count postcode nhs_region
## [3m[90m<chr>[39m[23m [3m[90m<date>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<int>[39m[23m [3m[90m<chr>[39m[23m [3m[90m<chr>[39m[23m
## [90m 1[39m 111 2020-03-18 fema… miss… e380000… nhs_glo… 1 gl34fe South West
## [90m 2[39m 111 2020-03-18 fema… miss… e380001… nhs_sou… 1 ne325nn North Eas…
## [90m 3[39m 111 2020-03-18 fema… 0-18 e380000… nhs_air… 8 bd57jr North Eas…
## [90m 4[39m 111 2020-03-18 fema… 0-18 e380000… nhs_ash… 7 tn254ab South East
## [90m 5[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 35 rm13ae London
## [90m 6[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 9 n111np London
## [90m 7[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bar… 11 s752py North Eas…
## [90m 8[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bas… 19 ss143hg East of E…
## [90m 9[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bas… 6 dn227xf North Eas…
## [90m10[39m 111 2020-03-18 fema… 0-18 e380000… nhs_bat… 9 ba25rp South West
## [90m# … with 428,463 more rows, and 2 more variables: day [3m[90m<int>[90m[23m, weekday [3m[90m<fct>[90m[23m[39mWe also import demographics data for NHS regions in England, used later in our analysis:
path <- here::here("data", "csv", "nhs_region_population_2018.csv")
nhs_region_pop <- rio::import(path) %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
nhs_region_pop$nhs_region <- gsub(" Of ", " of ", nhs_region_pop$nhs_region)
nhs_region_pop$nhs_region <- gsub(" And ", " and ", nhs_region_pop$nhs_region)
nhs_region_pop
## nhs_region variable value
## 1 North West 0-18 0.22538599
## 2 North East and Yorkshire 0-18 0.21876449
## 3 Midlands 0-18 0.22564656
## 4 East of England 0-18 0.22810783
## 5 London 0-18 0.23764782
## 6 South East 0-18 0.22458811
## 7 South West 0-18 0.20799797
## 8 North West 19-69 0.64274078
## 9 North East and Yorkshire 19-69 0.64437753
## 10 Midlands 19-69 0.63876675
## 11 East of England 19-69 0.63034229
## 12 London 19-69 0.67820084
## 13 South East 19-69 0.63267336
## 14 South West 19-69 0.63176131
## 15 North West 70-120 0.13187323
## 16 North East and Yorkshire 70-120 0.13685797
## 17 Midlands 70-120 0.13558669
## 18 East of England 70-120 0.14154988
## 19 London 70-120 0.08415135
## 20 South East 70-120 0.14273853
## 21 South West 70-120 0.16024072Finally, we import publically available deaths per NHS region:
dth <- import_deaths() %>%
mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))
#truncation to account for reporting delay
delay_max <- 21
dth$nhs_region <- gsub(" Of ", " of ", dth$nhs_region)
dth$nhs_region <- gsub(" And ", " and ", dth$nhs_region)
dth
## date_report nhs_region deaths
## 1 2020-03-01 East of England 0
## 2 2020-03-02 East of England 1
## 3 2020-03-03 East of England 0
## 4 2020-03-04 East of England 0
## 5 2020-03-05 East of England 0
## 6 2020-03-06 East of England 1
## 7 2020-03-07 East of England 0
## 8 2020-03-08 East of England 0
## 9 2020-03-09 East of England 1
## 10 2020-03-10 East of England 0
## 11 2020-03-11 East of England 0
## 12 2020-03-12 East of England 0
## 13 2020-03-13 East of England 1
## 14 2020-03-14 East of England 2
## 15 2020-03-15 East of England 2
## 16 2020-03-16 East of England 1
## 17 2020-03-17 East of England 1
## 18 2020-03-18 East of England 5
## 19 2020-03-19 East of England 4
## 20 2020-03-20 East of England 2
## 21 2020-03-21 East of England 11
## 22 2020-03-22 East of England 12
## 23 2020-03-23 East of England 11
## 24 2020-03-24 East of England 19
## 25 2020-03-25 East of England 26
## 26 2020-03-26 East of England 36
## 27 2020-03-27 East of England 38
## 28 2020-03-28 East of England 28
## 29 2020-03-29 East of England 43
## 30 2020-03-30 East of England 45
## 31 2020-03-31 East of England 70
## 32 2020-04-01 East of England 62
## 33 2020-04-02 East of England 65
## 34 2020-04-03 East of England 80
## 35 2020-04-04 East of England 71
## 36 2020-04-05 East of England 76
## 37 2020-04-06 East of England 71
## 38 2020-04-07 East of England 93
## 39 2020-04-08 East of England 111
## 40 2020-04-09 East of England 87
## 41 2020-04-10 East of England 74
## 42 2020-04-11 East of England 92
## 43 2020-04-12 East of England 100
## 44 2020-04-13 East of England 78
## 45 2020-04-14 East of England 61
## 46 2020-04-15 East of England 82
## 47 2020-04-16 East of England 74
## 48 2020-04-17 East of England 86
## 49 2020-04-18 East of England 64
## 50 2020-04-19 East of England 67
## 51 2020-04-20 East of England 67
## 52 2020-04-21 East of England 75
## 53 2020-04-22 East of England 67
## 54 2020-04-23 East of England 49
## 55 2020-04-24 East of England 66
## 56 2020-04-25 East of England 54
## 57 2020-04-26 East of England 48
## 58 2020-04-27 East of England 46
## 59 2020-04-28 East of England 58
## 60 2020-04-29 East of England 32
## 61 2020-04-30 East of England 45
## 62 2020-05-01 East of England 49
## 63 2020-05-02 East of England 29
## 64 2020-05-03 East of England 41
## 65 2020-05-04 East of England 19
## 66 2020-05-05 East of England 36
## 67 2020-05-06 East of England 31
## 68 2020-05-07 East of England 33
## 69 2020-05-08 East of England 33
## 70 2020-05-09 East of England 29
## 71 2020-05-10 East of England 22
## 72 2020-05-11 East of England 18
## 73 2020-05-12 East of England 21
## 74 2020-05-13 East of England 27
## 75 2020-05-14 East of England 26
## 76 2020-05-15 East of England 19
## 77 2020-05-16 East of England 26
## 78 2020-05-17 East of England 17
## 79 2020-05-18 East of England 25
## 80 2020-05-19 East of England 15
## 81 2020-05-20 East of England 26
## 82 2020-05-21 East of England 21
## 83 2020-05-22 East of England 13
## 84 2020-05-23 East of England 12
## 85 2020-05-24 East of England 17
## 86 2020-05-25 East of England 25
## 87 2020-05-26 East of England 14
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## 89 2020-05-28 East of England 17
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## 91 2020-05-30 East of England 9
## 92 2020-05-31 East of England 8
## 93 2020-06-01 East of England 17
## 94 2020-06-02 East of England 14
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## 102 2020-06-10 East of England 8
## 103 2020-06-11 East of England 1
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## 108 2020-06-16 East of England 3
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## 111 2020-06-19 East of England 7
## 112 2020-06-20 East of England 4
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## 114 2020-06-22 East of England 6
## 115 2020-06-23 East of England 5
## 116 2020-06-24 East of England 4
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## 120 2020-06-28 East of England 8
## 121 2020-06-29 East of England 4
## 122 2020-06-30 East of England 5
## 123 2020-07-01 East of England 2
## 124 2020-07-02 East of England 5
## 125 2020-07-03 East of England 0
## 126 2020-07-04 East of England 3
## 127 2020-07-05 East of England 1
## 128 2020-07-06 East of England 2
## 129 2020-07-07 East of England 2
## 130 2020-07-08 East of England 0
## 131 2020-07-09 East of England 8
## 132 2020-07-10 East of England 4
## 133 2020-07-11 East of England 2
## 134 2020-07-12 East of England 1
## 135 2020-07-13 East of England 8
## 136 2020-07-14 East of England 2
## 137 2020-07-15 East of England 0
## 138 2020-07-16 East of England 0
## 139 2020-07-17 East of England 0
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## 143 2020-07-21 East of England 1
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## 146 2020-07-24 East of England 1
## 147 2020-07-25 East of England 0
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## 149 2020-07-27 East of England 1
## 150 2020-07-28 East of England 2
## 151 2020-07-29 East of England 0
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## 171 2020-08-18 East of England 2
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## 173 2020-08-20 East of England 1
## 174 2020-08-21 East of England 0
## 175 2020-08-22 East of England 1
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## 179 2020-08-26 East of England 1
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## 189 2020-09-05 East of England 0
## 190 2020-09-06 East of England 1
## 191 2020-09-07 East of England 0
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## 193 2020-09-09 East of England 0
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## 195 2020-09-11 East of England 0
## 196 2020-09-12 East of England 0
## 197 2020-09-13 East of England 1
## 198 2020-09-14 East of England 1
## 199 2020-09-15 East of England 0
## 200 2020-09-16 East of England 0
## 201 2020-09-17 East of England 0
## 202 2020-09-18 East of England 0
## 203 2020-09-19 East of England 0
## 204 2020-09-20 East of England 2
## 205 2020-09-21 East of England 0
## 206 2020-09-22 East of England 2
## 207 2020-09-23 East of England 1
## 208 2020-09-24 East of England 0
## 209 2020-09-25 East of England 1
## 210 2020-09-26 East of England 1
## 211 2020-09-27 East of England 1
## 212 2020-09-28 East of England 2
## 213 2020-09-29 East of England 2
## 214 2020-09-30 East of England 2
## 215 2020-10-01 East of England 2
## 216 2020-10-02 East of England 1
## 217 2020-10-03 East of England 1
## 218 2020-10-04 East of England 0
## 219 2020-10-05 East of England 0
## 220 2020-10-06 East of England 4
## 221 2020-10-07 East of England 6
## 222 2020-10-08 East of England 3
## 223 2020-10-09 East of England 1
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## 225 2020-10-11 East of England 2
## 226 2020-10-12 East of England 2
## 227 2020-10-13 East of England 1
## 228 2020-10-14 East of England 3
## 229 2020-10-15 East of England 4
## 230 2020-10-16 East of England 5
## 231 2020-10-17 East of England 6
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## 233 2020-10-19 East of England 5
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## 235 2020-10-21 East of England 7
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## 238 2020-10-24 East of England 1
## 239 2020-10-25 East of England 10
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## 243 2020-10-29 East of England 10
## 244 2020-10-30 East of England 12
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## 247 2020-11-02 East of England 9
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## 1769 2020-04-23 South East 57
## 1770 2020-04-24 South East 64
## 1771 2020-04-25 South East 51
## 1772 2020-04-26 South East 51
## 1773 2020-04-27 South East 41
## 1774 2020-04-28 South East 40
## 1775 2020-04-29 South East 47
## 1776 2020-04-30 South East 29
## 1777 2020-05-01 South East 37
## 1778 2020-05-02 South East 36
## 1779 2020-05-03 South East 17
## 1780 2020-05-04 South East 35
## 1781 2020-05-05 South East 29
## 1782 2020-05-06 South East 25
## 1783 2020-05-07 South East 27
## 1784 2020-05-08 South East 26
## 1785 2020-05-09 South East 28
## 1786 2020-05-10 South East 19
## 1787 2020-05-11 South East 25
## 1788 2020-05-12 South East 27
## 1789 2020-05-13 South East 18
## 1790 2020-05-14 South East 32
## 1791 2020-05-15 South East 25
## 1792 2020-05-16 South East 22
## 1793 2020-05-17 South East 18
## 1794 2020-05-18 South East 23
## 1795 2020-05-19 South East 12
## 1796 2020-05-20 South East 22
## 1797 2020-05-21 South East 15
## 1798 2020-05-22 South East 17
## 1799 2020-05-23 South East 21
## 1800 2020-05-24 South East 17
## 1801 2020-05-25 South East 13
## 1802 2020-05-26 South East 19
## 1803 2020-05-27 South East 19
## 1804 2020-05-28 South East 12
## 1805 2020-05-29 South East 22
## 1806 2020-05-30 South East 8
## 1807 2020-05-31 South East 12
## 1808 2020-06-01 South East 11
## 1809 2020-06-02 South East 13
## 1810 2020-06-03 South East 18
## 1811 2020-06-04 South East 11
## 1812 2020-06-05 South East 11
## 1813 2020-06-06 South East 10
## 1814 2020-06-07 South East 12
## 1815 2020-06-08 South East 8
## 1816 2020-06-09 South East 11
## 1817 2020-06-10 South East 11
## 1818 2020-06-11 South East 5
## 1819 2020-06-12 South East 6
## 1820 2020-06-13 South East 7
## 1821 2020-06-14 South East 7
## 1822 2020-06-15 South East 8
## 1823 2020-06-16 South East 14
## 1824 2020-06-17 South East 10
## 1825 2020-06-18 South East 4
## 1826 2020-06-19 South East 7
## 1827 2020-06-20 South East 5
## 1828 2020-06-21 South East 3
## 1829 2020-06-22 South East 2
## 1830 2020-06-23 South East 9
## 1831 2020-06-24 South East 7
## 1832 2020-06-25 South East 5
## 1833 2020-06-26 South East 8
## 1834 2020-06-27 South East 9
## 1835 2020-06-28 South East 6
## 1836 2020-06-29 South East 5
## 1837 2020-06-30 South East 5
## 1838 2020-07-01 South East 2
## 1839 2020-07-02 South East 8
## 1840 2020-07-03 South East 3
## 1841 2020-07-04 South East 6
## 1842 2020-07-05 South East 5
## 1843 2020-07-06 South East 4
## 1844 2020-07-07 South East 6
## 1845 2020-07-08 South East 3
## 1846 2020-07-09 South East 7
## 1847 2020-07-10 South East 3
## 1848 2020-07-11 South East 4
## 1849 2020-07-12 South East 5
## 1850 2020-07-13 South East 5
## 1851 2020-07-14 South East 5
## 1852 2020-07-15 South East 6
## 1853 2020-07-16 South East 3
## 1854 2020-07-17 South East 1
## 1855 2020-07-18 South East 5
## 1856 2020-07-19 South East 2
## 1857 2020-07-20 South East 6
## 1858 2020-07-21 South East 4
## 1859 2020-07-22 South East 2
## 1860 2020-07-23 South East 3
## 1861 2020-07-24 South East 1
## 1862 2020-07-25 South East 1
## 1863 2020-07-26 South East 3
## 1864 2020-07-27 South East 1
## 1865 2020-07-28 South East 3
## 1866 2020-07-29 South East 2
## 1867 2020-07-30 South East 3
## 1868 2020-07-31 South East 1
## 1869 2020-08-01 South East 2
## 1870 2020-08-02 South East 4
## 1871 2020-08-03 South East 0
## 1872 2020-08-04 South East 0
## 1873 2020-08-05 South East 0
## 1874 2020-08-06 South East 2
## 1875 2020-08-07 South East 0
## 1876 2020-08-08 South East 2
## 1877 2020-08-09 South East 0
## 1878 2020-08-10 South East 2
## 1879 2020-08-11 South East 1
## 1880 2020-08-12 South East 1
## 1881 2020-08-13 South East 0
## 1882 2020-08-14 South East 0
## 1883 2020-08-15 South East 2
## 1884 2020-08-16 South East 1
## 1885 2020-08-17 South East 0
## 1886 2020-08-18 South East 2
## 1887 2020-08-19 South East 1
## 1888 2020-08-20 South East 0
## 1889 2020-08-21 South East 0
## 1890 2020-08-22 South East 0
## 1891 2020-08-23 South East 1
## 1892 2020-08-24 South East 0
## 1893 2020-08-25 South East 1
## 1894 2020-08-26 South East 0
## 1895 2020-08-27 South East 1
## 1896 2020-08-28 South East 2
## 1897 2020-08-29 South East 1
## 1898 2020-08-30 South East 0
## 1899 2020-08-31 South East 2
## 1900 2020-09-01 South East 1
## 1901 2020-09-02 South East 1
## 1902 2020-09-03 South East 0
## 1903 2020-09-04 South East 1
## 1904 2020-09-05 South East 0
## 1905 2020-09-06 South East 1
## 1906 2020-09-07 South East 0
## 1907 2020-09-08 South East 0
## 1908 2020-09-09 South East 0
## 1909 2020-09-10 South East 1
## 1910 2020-09-11 South East 1
## 1911 2020-09-12 South East 0
## 1912 2020-09-13 South East 3
## 1913 2020-09-14 South East 1
## 1914 2020-09-15 South East 2
## 1915 2020-09-16 South East 2
## 1916 2020-09-17 South East 3
## 1917 2020-09-18 South East 1
## 1918 2020-09-19 South East 1
## 1919 2020-09-20 South East 0
## 1920 2020-09-21 South East 3
## 1921 2020-09-22 South East 0
## 1922 2020-09-23 South East 2
## 1923 2020-09-24 South East 1
## 1924 2020-09-25 South East 3
## 1925 2020-09-26 South East 2
## 1926 2020-09-27 South East 2
## 1927 2020-09-28 South East 6
## 1928 2020-09-29 South East 3
## 1929 2020-09-30 South East 4
## 1930 2020-10-01 South East 4
## 1931 2020-10-02 South East 2
## 1932 2020-10-03 South East 1
## 1933 2020-10-04 South East 1
## 1934 2020-10-05 South East 2
## 1935 2020-10-06 South East 1
## 1936 2020-10-07 South East 4
## 1937 2020-10-08 South East 1
## 1938 2020-10-09 South East 1
## 1939 2020-10-10 South East 3
## 1940 2020-10-11 South East 3
## 1941 2020-10-12 South East 4
## 1942 2020-10-13 South East 2
## 1943 2020-10-14 South East 2
## 1944 2020-10-15 South East 3
## 1945 2020-10-16 South East 2
## 1946 2020-10-17 South East 3
## 1947 2020-10-18 South East 4
## 1948 2020-10-19 South East 7
## 1949 2020-10-20 South East 8
## 1950 2020-10-21 South East 9
## 1951 2020-10-22 South East 5
## 1952 2020-10-23 South East 7
## 1953 2020-10-24 South East 5
## 1954 2020-10-25 South East 9
## 1955 2020-10-26 South East 13
## 1956 2020-10-27 South East 10
## 1957 2020-10-28 South East 10
## 1958 2020-10-29 South East 7
## 1959 2020-10-30 South East 6
## 1960 2020-10-31 South East 15
## 1961 2020-11-01 South East 18
## 1962 2020-11-02 South East 13
## 1963 2020-11-03 South East 16
## 1964 2020-11-04 South East 10
## 1965 2020-11-05 South East 10
## 1966 2020-11-06 South East 16
## 1967 2020-11-07 South East 17
## 1968 2020-11-08 South East 18
## 1969 2020-11-09 South East 19
## 1970 2020-11-10 South East 20
## 1971 2020-11-11 South East 20
## 1972 2020-11-12 South East 20
## 1973 2020-11-13 South East 12
## 1974 2020-11-14 South East 24
## 1975 2020-11-15 South East 25
## 1976 2020-11-16 South East 22
## 1977 2020-11-17 South East 23
## 1978 2020-11-18 South East 26
## 1979 2020-11-19 South East 21
## 1980 2020-11-20 South East 18
## 1981 2020-11-21 South East 23
## 1982 2020-11-22 South East 30
## 1983 2020-11-23 South East 29
## 1984 2020-11-24 South East 26
## 1985 2020-11-25 South East 42
## 1986 2020-11-26 South East 30
## 1987 2020-11-27 South East 31
## 1988 2020-11-28 South East 24
## 1989 2020-11-29 South East 37
## 1990 2020-11-30 South East 23
## 1991 2020-12-01 South East 29
## 1992 2020-12-02 South East 33
## 1993 2020-12-03 South East 36
## 1994 2020-12-04 South East 41
## 1995 2020-12-05 South East 36
## 1996 2020-12-06 South East 32
## 1997 2020-12-07 South East 25
## 1998 2020-12-08 South East 43
## 1999 2020-12-09 South East 44
## 2000 2020-12-10 South East 38
## 2001 2020-12-11 South East 48
## 2002 2020-12-12 South East 40
## 2003 2020-12-13 South East 41
## 2004 2020-12-14 South East 38
## 2005 2020-12-15 South East 50
## 2006 2020-12-16 South East 45
## 2007 2020-12-17 South East 54
## 2008 2020-12-18 South East 48
## 2009 2020-12-19 South East 43
## 2010 2020-12-20 South East 57
## 2011 2020-12-21 South East 66
## 2012 2020-12-22 South East 62
## 2013 2020-12-23 South East 70
## 2014 2020-12-24 South East 56
## 2015 2020-12-25 South East 71
## 2016 2020-12-26 South East 75
## 2017 2020-12-27 South East 76
## 2018 2020-12-28 South East 81
## 2019 2020-12-29 South East 78
## 2020 2020-12-30 South East 92
## 2021 2020-12-31 South East 91
## 2022 2021-01-01 South East 60
## 2023 2021-01-02 South East 94
## 2024 2021-01-03 South East 79
## 2025 2021-01-04 South East 105
## 2026 2021-01-05 South East 106
## 2027 2021-01-06 South East 121
## 2028 2021-01-07 South East 113
## 2029 2021-01-08 South East 124
## 2030 2021-01-09 South East 114
## 2031 2021-01-10 South East 122
## 2032 2021-01-11 South East 128
## 2033 2021-01-12 South East 166
## 2034 2021-01-13 South East 128
## 2035 2021-01-14 South East 137
## 2036 2021-01-15 South East 125
## 2037 2021-01-16 South East 152
## 2038 2021-01-17 South East 163
## 2039 2021-01-18 South East 148
## 2040 2021-01-19 South East 153
## 2041 2021-01-20 South East 133
## 2042 2021-01-21 South East 129
## 2043 2021-01-22 South East 129
## 2044 2021-01-23 South East 120
## 2045 2021-01-24 South East 115
## 2046 2021-01-25 South East 112
## 2047 2021-01-26 South East 130
## 2048 2021-01-27 South East 110
## 2049 2021-01-28 South East 127
## 2050 2021-01-29 South East 106
## 2051 2021-01-30 South East 89
## 2052 2021-01-31 South East 90
## 2053 2021-02-01 South East 86
## 2054 2021-02-02 South East 74
## 2055 2021-02-03 South East 79
## 2056 2021-02-04 South East 47
## 2057 2021-02-05 South East 44
## 2058 2021-02-06 South East 5
## 2059 2020-03-01 South West 0
## 2060 2020-03-02 South West 0
## 2061 2020-03-03 South West 0
## 2062 2020-03-04 South West 0
## 2063 2020-03-05 South West 0
## 2064 2020-03-06 South West 0
## 2065 2020-03-07 South West 0
## 2066 2020-03-08 South West 0
## 2067 2020-03-09 South West 0
## 2068 2020-03-10 South West 0
## 2069 2020-03-11 South West 1
## 2070 2020-03-12 South West 0
## 2071 2020-03-13 South West 0
## 2072 2020-03-14 South West 1
## 2073 2020-03-15 South West 0
## 2074 2020-03-16 South West 0
## 2075 2020-03-17 South West 2
## 2076 2020-03-18 South West 2
## 2077 2020-03-19 South West 4
## 2078 2020-03-20 South West 3
## 2079 2020-03-21 South West 6
## 2080 2020-03-22 South West 7
## 2081 2020-03-23 South West 8
## 2082 2020-03-24 South West 7
## 2083 2020-03-25 South West 9
## 2084 2020-03-26 South West 11
## 2085 2020-03-27 South West 13
## 2086 2020-03-28 South West 21
## 2087 2020-03-29 South West 18
## 2088 2020-03-30 South West 23
## 2089 2020-03-31 South West 23
## 2090 2020-04-01 South West 21
## 2091 2020-04-02 South West 23
## 2092 2020-04-03 South West 30
## 2093 2020-04-04 South West 42
## 2094 2020-04-05 South West 32
## 2095 2020-04-06 South West 34
## 2096 2020-04-07 South West 39
## 2097 2020-04-08 South West 47
## 2098 2020-04-09 South West 24
## 2099 2020-04-10 South West 46
## 2100 2020-04-11 South West 43
## 2101 2020-04-12 South West 23
## 2102 2020-04-13 South West 27
## 2103 2020-04-14 South West 24
## 2104 2020-04-15 South West 32
## 2105 2020-04-16 South West 29
## 2106 2020-04-17 South West 33
## 2107 2020-04-18 South West 25
## 2108 2020-04-19 South West 31
## 2109 2020-04-20 South West 26
## 2110 2020-04-21 South West 26
## 2111 2020-04-22 South West 23
## 2112 2020-04-23 South West 17
## 2113 2020-04-24 South West 19
## 2114 2020-04-25 South West 15
## 2115 2020-04-26 South West 27
## 2116 2020-04-27 South West 13
## 2117 2020-04-28 South West 17
## 2118 2020-04-29 South West 15
## 2119 2020-04-30 South West 26
## 2120 2020-05-01 South West 6
## 2121 2020-05-02 South West 7
## 2122 2020-05-03 South West 10
## 2123 2020-05-04 South West 17
## 2124 2020-05-05 South West 14
## 2125 2020-05-06 South West 19
## 2126 2020-05-07 South West 16
## 2127 2020-05-08 South West 6
## 2128 2020-05-09 South West 11
## 2129 2020-05-10 South West 5
## 2130 2020-05-11 South West 8
## 2131 2020-05-12 South West 7
## 2132 2020-05-13 South West 7
## 2133 2020-05-14 South West 6
## 2134 2020-05-15 South West 4
## 2135 2020-05-16 South West 4
## 2136 2020-05-17 South West 6
## 2137 2020-05-18 South West 4
## 2138 2020-05-19 South West 6
## 2139 2020-05-20 South West 1
## 2140 2020-05-21 South West 9
## 2141 2020-05-22 South West 7
## 2142 2020-05-23 South West 6
## 2143 2020-05-24 South West 3
## 2144 2020-05-25 South West 8
## 2145 2020-05-26 South West 11
## 2146 2020-05-27 South West 5
## 2147 2020-05-28 South West 10
## 2148 2020-05-29 South West 7
## 2149 2020-05-30 South West 3
## 2150 2020-05-31 South West 2
## 2151 2020-06-01 South West 7
## 2152 2020-06-02 South West 2
## 2153 2020-06-03 South West 7
## 2154 2020-06-04 South West 2
## 2155 2020-06-05 South West 2
## 2156 2020-06-06 South West 1
## 2157 2020-06-07 South West 3
## 2158 2020-06-08 South West 3
## 2159 2020-06-09 South West 0
## 2160 2020-06-10 South West 1
## 2161 2020-06-11 South West 2
## 2162 2020-06-12 South West 2
## 2163 2020-06-13 South West 2
## 2164 2020-06-14 South West 0
## 2165 2020-06-15 South West 2
## 2166 2020-06-16 South West 2
## 2167 2020-06-17 South West 0
## 2168 2020-06-18 South West 0
## 2169 2020-06-19 South West 0
## 2170 2020-06-20 South West 2
## 2171 2020-06-21 South West 0
## 2172 2020-06-22 South West 1
## 2173 2020-06-23 South West 1
## 2174 2020-06-24 South West 1
## 2175 2020-06-25 South West 0
## 2176 2020-06-26 South West 3
## 2177 2020-06-27 South West 0
## 2178 2020-06-28 South West 0
## 2179 2020-06-29 South West 1
## 2180 2020-06-30 South West 0
## 2181 2020-07-01 South West 0
## 2182 2020-07-02 South West 0
## 2183 2020-07-03 South West 0
## 2184 2020-07-04 South West 0
## 2185 2020-07-05 South West 1
## 2186 2020-07-06 South West 0
## 2187 2020-07-07 South West 0
## 2188 2020-07-08 South West 2
## 2189 2020-07-09 South West 0
## 2190 2020-07-10 South West 1
## 2191 2020-07-11 South West 0
## 2192 2020-07-12 South West 0
## 2193 2020-07-13 South West 1
## 2194 2020-07-14 South West 0
## 2195 2020-07-15 South West 0
## 2196 2020-07-16 South West 0
## 2197 2020-07-17 South West 1
## 2198 2020-07-18 South West 0
## 2199 2020-07-19 South West 0
## 2200 2020-07-20 South West 0
## 2201 2020-07-21 South West 0
## 2202 2020-07-22 South West 0
## 2203 2020-07-23 South West 0
## 2204 2020-07-24 South West 0
## 2205 2020-07-25 South West 0
## 2206 2020-07-26 South West 0
## 2207 2020-07-27 South West 0
## 2208 2020-07-28 South West 0
## 2209 2020-07-29 South West 0
## 2210 2020-07-30 South West 1
## 2211 2020-07-31 South West 0
## 2212 2020-08-01 South West 0
## 2213 2020-08-02 South West 0
## 2214 2020-08-03 South West 0
## 2215 2020-08-04 South West 0
## 2216 2020-08-05 South West 0
## 2217 2020-08-06 South West 0
## 2218 2020-08-07 South West 0
## 2219 2020-08-08 South West 0
## 2220 2020-08-09 South West 0
## 2221 2020-08-10 South West 0
## 2222 2020-08-11 South West 0
## 2223 2020-08-12 South West 0
## 2224 2020-08-13 South West 0
## 2225 2020-08-14 South West 1
## 2226 2020-08-15 South West 0
## 2227 2020-08-16 South West 0
## 2228 2020-08-17 South West 2
## 2229 2020-08-18 South West 0
## 2230 2020-08-19 South West 0
## 2231 2020-08-20 South West 0
## 2232 2020-08-21 South West 0
## 2233 2020-08-22 South West 0
## 2234 2020-08-23 South West 0
## 2235 2020-08-24 South West 0
## 2236 2020-08-25 South West 1
## 2237 2020-08-26 South West 0
## 2238 2020-08-27 South West 1
## 2239 2020-08-28 South West 0
## 2240 2020-08-29 South West 0
## 2241 2020-08-30 South West 0
## 2242 2020-08-31 South West 0
## 2243 2020-09-01 South West 0
## 2244 2020-09-02 South West 0
## 2245 2020-09-03 South West 0
## 2246 2020-09-04 South West 0
## 2247 2020-09-05 South West 0
## 2248 2020-09-06 South West 0
## 2249 2020-09-07 South West 0
## 2250 2020-09-08 South West 1
## 2251 2020-09-09 South West 0
## 2252 2020-09-10 South West 0
## 2253 2020-09-11 South West 0
## 2254 2020-09-12 South West 0
## 2255 2020-09-13 South West 1
## 2256 2020-09-14 South West 0
## 2257 2020-09-15 South West 0
## 2258 2020-09-16 South West 0
## 2259 2020-09-17 South West 1
## 2260 2020-09-18 South West 0
## 2261 2020-09-19 South West 0
## 2262 2020-09-20 South West 1
## 2263 2020-09-21 South West 0
## 2264 2020-09-22 South West 0
## 2265 2020-09-23 South West 0
## 2266 2020-09-24 South West 1
## 2267 2020-09-25 South West 0
## 2268 2020-09-26 South West 0
## 2269 2020-09-27 South West 0
## 2270 2020-09-28 South West 0
## 2271 2020-09-29 South West 0
## 2272 2020-09-30 South West 0
## 2273 2020-10-01 South West 0
## 2274 2020-10-02 South West 1
## 2275 2020-10-03 South West 0
## 2276 2020-10-04 South West 0
## 2277 2020-10-05 South West 0
## 2278 2020-10-06 South West 1
## 2279 2020-10-07 South West 0
## 2280 2020-10-08 South West 1
## 2281 2020-10-09 South West 1
## 2282 2020-10-10 South West 0
## 2283 2020-10-11 South West 4
## 2284 2020-10-12 South West 2
## 2285 2020-10-13 South West 0
## 2286 2020-10-14 South West 3
## 2287 2020-10-15 South West 1
## 2288 2020-10-16 South West 2
## 2289 2020-10-17 South West 8
## 2290 2020-10-18 South West 2
## 2291 2020-10-19 South West 2
## 2292 2020-10-20 South West 3
## 2293 2020-10-21 South West 6
## 2294 2020-10-22 South West 6
## 2295 2020-10-23 South West 5
## 2296 2020-10-24 South West 5
## 2297 2020-10-25 South West 5
## 2298 2020-10-26 South West 7
## 2299 2020-10-27 South West 6
## 2300 2020-10-28 South West 8
## 2301 2020-10-29 South West 11
## 2302 2020-10-30 South West 8
## 2303 2020-10-31 South West 4
## 2304 2020-11-01 South West 5
## 2305 2020-11-02 South West 11
## 2306 2020-11-03 South West 7
## 2307 2020-11-04 South West 8
## 2308 2020-11-05 South West 5
## 2309 2020-11-06 South West 11
## 2310 2020-11-07 South West 10
## 2311 2020-11-08 South West 10
## 2312 2020-11-09 South West 12
## 2313 2020-11-10 South West 6
## 2314 2020-11-11 South West 13
## 2315 2020-11-12 South West 17
## 2316 2020-11-13 South West 9
## 2317 2020-11-14 South West 8
## 2318 2020-11-15 South West 16
## 2319 2020-11-16 South West 18
## 2320 2020-11-17 South West 17
## 2321 2020-11-18 South West 26
## 2322 2020-11-19 South West 15
## 2323 2020-11-20 South West 25
## 2324 2020-11-21 South West 25
## 2325 2020-11-22 South West 24
## 2326 2020-11-23 South West 14
## 2327 2020-11-24 South West 20
## 2328 2020-11-25 South West 25
## 2329 2020-11-26 South West 16
## 2330 2020-11-27 South West 21
## 2331 2020-11-28 South West 35
## 2332 2020-11-29 South West 15
## 2333 2020-11-30 South West 21
## 2334 2020-12-01 South West 19
## 2335 2020-12-02 South West 15
## 2336 2020-12-03 South West 14
## 2337 2020-12-04 South West 20
## 2338 2020-12-05 South West 17
## 2339 2020-12-06 South West 13
## 2340 2020-12-07 South West 16
## 2341 2020-12-08 South West 19
## 2342 2020-12-09 South West 21
## 2343 2020-12-10 South West 20
## 2344 2020-12-11 South West 20
## 2345 2020-12-12 South West 15
## 2346 2020-12-13 South West 19
## 2347 2020-12-14 South West 20
## 2348 2020-12-15 South West 20
## 2349 2020-12-16 South West 9
## 2350 2020-12-17 South West 26
## 2351 2020-12-18 South West 11
## 2352 2020-12-19 South West 22
## 2353 2020-12-20 South West 19
## 2354 2020-12-21 South West 21
## 2355 2020-12-22 South West 10
## 2356 2020-12-23 South West 16
## 2357 2020-12-24 South West 18
## 2358 2020-12-25 South West 19
## 2359 2020-12-26 South West 24
## 2360 2020-12-27 South West 24
## 2361 2020-12-28 South West 20
## 2362 2020-12-29 South West 20
## 2363 2020-12-30 South West 17
## 2364 2020-12-31 South West 27
## 2365 2021-01-01 South West 29
## 2366 2021-01-02 South West 24
## 2367 2021-01-03 South West 28
## 2368 2021-01-04 South West 30
## 2369 2021-01-05 South West 31
## 2370 2021-01-06 South West 24
## 2371 2021-01-07 South West 29
## 2372 2021-01-08 South West 33
## 2373 2021-01-09 South West 26
## 2374 2021-01-10 South West 31
## 2375 2021-01-11 South West 36
## 2376 2021-01-12 South West 48
## 2377 2021-01-13 South West 40
## 2378 2021-01-14 South West 32
## 2379 2021-01-15 South West 44
## 2380 2021-01-16 South West 54
## 2381 2021-01-17 South West 32
## 2382 2021-01-18 South West 42
## 2383 2021-01-19 South West 43
## 2384 2021-01-20 South West 62
## 2385 2021-01-21 South West 54
## 2386 2021-01-22 South West 57
## 2387 2021-01-23 South West 52
## 2388 2021-01-24 South West 59
## 2389 2021-01-25 South West 55
## 2390 2021-01-26 South West 41
## 2391 2021-01-27 South West 39
## 2392 2021-01-28 South West 48
## 2393 2021-01-29 South West 41
## 2394 2021-01-30 South West 35
## 2395 2021-01-31 South West 34
## 2396 2021-02-01 South West 27
## 2397 2021-02-02 South West 33
## 2398 2021-02-03 South West 28
## 2399 2021-02-04 South West 28
## 2400 2021-02-05 South West 12
## 2401 2021-02-06 South West 3We extract the completion date from the NHS Pathways file timestamp:
The completion date of the NHS Pathways data is Thursday 04 Feb 2021.
These are functions which will be used further in the analyses.
Function to estimate the generalised R-squared as the proportion of deviance explained by a given model:
## Function to calculate R2 for Poisson model
## not adjusted for model complexity but all models have the same DF here
Rsq <- function(x) {
1 - (x$deviance / x$null.deviance)
}Function to extract growth rates per region as well as halving times, and the associated 95% confidence intervals:
## function to extract the coefficients, find the level of the intercept,
## reconstruct the values of r, get confidence intervals
get_r <- function(model) {
## extract coefficients and conf int
out <- data.frame(r = coef(model)) %>%
rownames_to_column("var") %>%
cbind(confint(model)) %>%
filter(!grepl("day_of_week", var)) %>%
filter(grepl("day", var)) %>%
rename(lower_95 = "2.5 %",
upper_95 = "97.5 %") %>%
mutate(var = sub("day:", "", var))
## reconstruct values: intercept + region-coefficient
for (i in 2:nrow(out)) {
out[i, -1] <- out[1, -1] + out[i, -1]
}
## find the name of the intercept, restore regions names
out <- out %>%
mutate(nhs_region = model$xlevels$nhs_region) %>%
select(nhs_region, everything(), -var)
## find halving times
halving <- log(0.5) / out[,-1] %>%
rename(halving_t = r,
halving_t_lower_95 = lower_95,
halving_t_upper_95 = upper_95)
## set halving times with exclusion intervals to NA
no_halving <- out$lower_95 < 0 & out$upper_95 > 0
halving[no_halving, ] <- NA_real_
## return all data
cbind(out, halving)
}Functions used in the correlation analysis between NHS Pathways reports and deaths:
## Function to calculate Pearson's correlation between deaths and lagged
## reports. Note that `pearson` can be replaced with `spearman` for rank
## correlation.
getcor <- function(x, ndx) {
return(cor(x$deaths[ndx],
x$note_lag[ndx],
use = "complete.obs",
method = "pearson"))
}
## Catch if sample size throws an error
getcor2 <- possibly(getcor, otherwise = NA)
getboot <- function(x) {
result <- boot::boot.ci(boot::boot(x, getcor2, R = 1000),
type = "bca")
return(data.frame(n = sum(!is.na(x$note_lag) & !is.na(x$deaths)),
r = result$t0,
r_low = result$bca[4],
r_hi = result$bca[5]))
}Function to classify the day of the week into weekend, Monday, and the rest:
## Fn to add day of week
day_of_week <- function(df) {
df %>%
dplyr::mutate(day_of_week = lubridate::wday(date, label = TRUE)) %>%
dplyr::mutate(day_of_week = dplyr::case_when(
day_of_week %in% c("Sat", "Sun") ~ "weekend",
day_of_week %in% c("Mon") ~ "monday",
!(day_of_week %in% c("Sat", "Sun", "Mon")) ~ "rest_of_week"
) %>%
factor(levels = c("rest_of_week", "monday", "weekend")))
}Custom color palettes, color scales, and vectors of colors:
We look for temporal patterns in COVID-19 related 111/999 calls and 111 online reports. Analyses are broken down by NHS region. We also look for estimates of recent growth rate and associated doubling / halving time.
tab_date_region_all <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
dth %>%
mutate(trusted = case_when(date_report < max(dth$date_report)-delay_max ~ "Y",
date_report >= max(dth$date_report)-delay_max ~ "N"),
value = "Deaths",
vline = max(dth$date_report)-delay_max-1,
lab = "Truncated for reporting delay",
lab_pos_x = vline + 10,
lab_pos_y = 150,
lab_col = "darkgrey") %>%
rename(date = date_report,
n = deaths) %>%
bind_rows(
mutate(tab_date_region_all, value = "Reports",
trusted = "Y",
vline = as.Date("2020-03-23"),
lab = "Start of UK lockdown",
lab_pos_x = vline - 8,
lab_pos_y = 30200,
lab_col = "black")
) %>%
mutate(value = factor(value, levels = c("Reports","Deaths"))) -> dths_reports
plot_dth_report <-
ggplot(dths_reports, aes(date, n, colour = nhs_region)) +
# Add main points and lines, coloured by region and fade out deaths for excluded period
geom_point(aes(alpha = trusted)) +
geom_line(alpha = 0.2) +
geom_smooth(method = "loess", span = .5, color = "black") +
scale_colour_manual("", values = pal) +
scale_alpha_manual(values = c(0.3,1)) +
guides(alpha = F) +
# Add vertical markers for important dates with labels - different for each facet
ggnewscale::new_scale_colour() +
geom_vline(aes(xintercept = vline, col = value), lty = "solid") +
geom_text(aes(x = lab_pos_x, y = lab_pos_y, label = lab, col = value), size = 3) +
scale_colour_manual("",values = c("black","darkgrey"), guide = F) +
# Facet by deaths and reports
facet_grid(rows = vars(value), scales = "free_y", switch = "y") +
# Other formatting
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",strip.placement = "outside") +
rotate_x +
labs(x = NULL,
y = NULL)
plot_dth_reportWe plot the number of 111/999 calls and 111 online reports by age, and the proportion of 111/999 calls and 111 online reports by age. In the second graph, the vertical lines indicate the proportion of individuals residing in the corresponding NHS region who belong to the corresponding age group.
tab_date_region_age_all <- x %>%
filter(!is.na(nhs_region),
age != "missing") %>%
group_by(date, nhs_region, age) %>%
summarise(n = sum(count))
tab_date_region_age_all %>%
ggplot(aes(x = date, y = n, fill = age)) +
geom_col(position = "stack") +
scale_fill_manual(values = age.pal) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(fill = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Total daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)
tab_date_region_age_all <- tab_date_region_age_all %>%
group_by(date, nhs_region) %>%
summarise(tot = sum(n)) %>%
left_join(tab_date_region_age_all, by = c("date", "nhs_region")) %>%
mutate(prop_n = n/tot)
tab_date_region_age_all %>%
ggplot(aes(x = date, y = prop_n, color = age)) +
scale_color_manual(values = age.pal) +
geom_line() +
geom_point() +
geom_hline(data = nhs_region_pop, aes(yintercept = value, color = variable)) +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(color = guide_legend(title = "Age", ncol = 3)) +
labs(x = NULL,
y = "Proportion of daily reports by age") +
facet_wrap(~ nhs_region, ncol = 4)We fit quasi-Poisson GLMs for 14-day windows to get growth rates over time.
## set moving time window (1/2/3 weeks)
w <- 14
# create empty df
r_all_sliding <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding <- bind_rows(r_all_sliding, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding <- r_all_sliding %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))We examine the evolution of the growth rate by region over time.
# plot
plot_growth <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)From the growth rate, we derive R and examine its value through time.
# plot
plot_R <-
r_all_sliding %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
rotate_x +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
# strip.text.x = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "",
override.aes = list(fill = NA)),
fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))We repeat the above analysis, where we fit quasi-Poisson GLMs for 14-day windows to get growth rates over time, but apply this to each age group separately (0-18, 19-69, 70-120 years old).
We first run the analysis for 0-18 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_0_18 <- NULL
## make data for model
x_model_all_moving_0_18 <- x %>%
filter(!is.na(nhs_region),
age == "0-18") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_0_18$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_0_18 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_0_18 <- bind_rows(r_all_sliding_0_18, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_0_18 <- r_all_sliding_0_18 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_0_18 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_0_18 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_0_18 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_0_18 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then, we run the analysis for 19-69 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_19_69 <- NULL
## make data for model
x_model_all_moving_19_69 <- x %>%
filter(!is.na(nhs_region),
age == "19-69") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_19_69$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_19_69 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_19_69 <- bind_rows(r_all_sliding_19_69, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_19_69 <- r_all_sliding_19_69 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_19_69 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)"
) +
scale_colour_manual(values = pal)
R <- r_all_sliding_19_69 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_19_69 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_19_69 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Finally, we run the analysis for 70-120 years old.
## set moving time window (2 weeks)
w <- 14
# create empty df
r_all_sliding_70_120 <- NULL
## make data for model
x_model_all_moving_70_120 <- x %>%
filter(!is.na(nhs_region),
age == "70-120") %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving_70_120$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving_70_120 %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_70_120 <- bind_rows(r_all_sliding_70_120, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
#convert growth rates r to R0
r_all_sliding_70_120 <- r_all_sliding_70_120 %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)"
) +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_70_120 %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_70_120 %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_70_120 %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
fig2_3_70_120 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)"))) We combine the estimated growth rates and effective reproduction numbers into a single figure.
ggpubr::ggarrange(fig2_3_0_18,
fig2_3_19_69,
fig2_3_70_120,
nrow = 3,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom",
align = "hv") We want to explore the correlation between NHS Pathways reports and deaths, and assess the potential for reports to be used as an early warning system for disease resurgence.
Death data are publically available. We truncate the time series to avoid bias from reporting delay - we assume a conservative delay of three weeks.
We calculate Pearson’s correlation coefficient between deaths and NHS Pathways notifications using different lags. Confidence intervals are obtained using bootstrap. Note that results were also confirmed using Spearman’s rank correlation.
First we join the NHS Pathways and death data, and aggregate over all England:
## truncate death data for reporting delay
trunc_date <- max(dth$date_report) - delay_max
dth_trunc <- dth %>%
rename(date = date_report) %>%
filter(date <= trunc_date)
## join with notification data
all_data <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(count = sum(count, na.rm = T)) %>%
ungroup %>%
inner_join(dth_trunc,
by = c("date","nhs_region"))
all_tot <- all_data %>%
group_by(date) %>%
summarise(count = sum(count, na.rm = TRUE),
deaths = sum(deaths, na.rm = TRUE)) We calculate correlation with lagged NHS Pathways reports from 0 to 30 days behind deaths:
## Calculate all correlations + bootstrap CIs
lag_cor <- data.frame()
for (i in 0:30) {
## lag reports
summary <- all_tot %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI
getboot(.) %>%
mutate(lag = i)
lag_cor <- bind_rows(lag_cor, summary)
}
cor_vs_lag <- ggplot(lag_cor, aes(lag, r)) +
theme_bw() +
geom_ribbon(aes(ymin = r_low, ymax = r_hi), alpha = 0.2) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_point() +
geom_line() +
labs(x = "Lag between NHS pathways and death data (days)",
y = "Pearson's correlation") +
large_txt
cor_vs_lagThis analysis suggests that the best lag is 15 days. We then compare and plot the number of deaths reported against the number of NHS Pathways reports lagged by 15 days.
all_tot <- all_tot %>%
rename(date_death = date) %>%
mutate(note_lag = lag(count, lag_cor$lag[l_opt]),
note_lag_c = (note_lag - mean(note_lag, na.rm = T)),
date_note = lag(date_death,16))
lag_mod <- glm(deaths ~ note_lag, data = all_tot, family = "quasipoisson")
summary(lag_mod)
##
## Call:
## glm(formula = deaths ~ note_lag, family = "quasipoisson", data = all_tot)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -16.805 -14.262 -5.089 7.760 35.681
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.969e+00 7.003e-02 70.95 <2e-16 ***
## note_lag 1.334e-05 1.163e-06 11.47 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for quasipoisson family taken to be 182.6093)
##
## Null deviance: 66305 on 289 degrees of freedom
## Residual deviance: 48943 on 288 degrees of freedom
## (15 observations deleted due to missingness)
## AIC: NA
##
## Number of Fisher Scoring iterations: 5
exp(coefficients(lag_mod))
## (Intercept) note_lag
## 143.902353 1.000013
exp(confint(lag_mod))
## 2.5 % 97.5 %
## (Intercept) 125.105339 164.647242
## note_lag 1.000011 1.000016
Rsq(lag_mod)
## [1] 0.2618555
mod_fit <- as.data.frame(predict(lag_mod, type = "link", se.fit = TRUE)[1:2])
all_tot_pred <-
all_tot %>%
filter(!is.na(note_lag)) %>%
mutate(pred = mod_fit$fit,
pred.se = mod_fit$se.fit,
low = exp(pred - 1.96*pred.se),
hi = exp(pred + 1.96*pred.se))
glm_fit <- all_tot_pred %>%
filter(!is.na(note_lag)) %>%
ggplot(aes(x = note_lag, y = deaths)) +
geom_point() +
geom_line(aes(y = exp(pred))) +
geom_ribbon(aes(ymin = low, ymax = hi), alpha = 0.3, col = "grey") +
theme_bw() +
labs(y = "Daily number of\ndeaths reported",
x = "Daily number of NHS Pathways reports") +
large_txt
glm_fitThis is a comparison of gamma versus lognormal distribution for the serial interval used to convert r to R in our analysis. Both distributions are parameterised with mean 4.7 and standard deviation 2.9.
SI_param <- epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale, w = 0.5)
SI_distribution2 <- distcrete::distcrete("lnorm", interval = 1,
meanlog = log(4.7),
sdlog = log(2.9), w = 0.5)
SI_dist1 <- data.frame(x = SI_distribution$r(1e5))
SI_dist1 <- count(SI_dist1, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 30, 5)) +
theme_bw()
SI_dist2 <- data.frame(x = SI_distribution2$r(1e5))
SI_dist2 <- count(SI_dist2, x) %>%
ggplot() +
geom_col(aes(x = x, y = n)) +
labs(x = "Serial interval (days)", y = "Frequency") +
scale_x_continuous(breaks = seq(0, 200, 20), limits = c(0, 200)) +
theme_bw()
ggpubr::ggarrange(SI_dist1,
SI_dist2,
nrow = 1,
labels = "AUTO") We reproduce the window analysis with either a 7 or 21 days window for sensitivity purposes.
First with the 7 days window:
## set moving time window (1/2/3 weeks)
w <- 7
# create empty df
r_all_sliding_7days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_7days <- bind_rows(r_all_sliding_7days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_7days <- r_all_sliding_7days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)plot_R <- r_all_sliding_7days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_7days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_7days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_7 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))Then with the 21 days window:
## set moving time window (1/2/3 weeks)
w <- 21
# create empty df
r_all_sliding_21days <- NULL
## make data for model
x_model_all_moving <- x %>%
filter(!is.na(nhs_region)) %>%
group_by(date, nhs_region) %>%
summarise(n = sum(count))
unique_dates <- unique(x_model_all_moving$date)
for (i in 1:(length(unique_dates) - w)) {
date_i <- unique_dates[i]
date_i_max <- date_i + w
model_data <- x_model_all_moving %>%
filter(date >= date_i & date < date_i_max) %>%
mutate(day = as.integer(date - date_i)) %>%
day_of_week()
mod <- glm(n ~ day * nhs_region + day_of_week,
data = model_data,
family = 'quasipoisson')
# get growth rate
r <- get_r(mod)
r$w_min <- date_i
r$w_max <- date_i_max
# combine all estimates
r_all_sliding_21days <- bind_rows(r_all_sliding_21days, r)
}
#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
shape = SI_param$shape,
scale = SI_param$scale,
w = 0.5)
#convert growth rates r to R0
r_all_sliding_21days <- r_all_sliding_21days %>%
mutate(R = epitrix::r2R0(r, SI_distribution),
R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))# plot
plot_growth <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = r)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 0, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated daily growth rate (r)") +
scale_colour_manual(values = pal)# plot
plot_R <-
r_all_sliding_21days %>%
ggplot(aes(x = w_max, y = R)) +
geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(yintercept = 1, linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "",
y = "Estimated effective reproduction\nnumber (Re)") +
scale_colour_manual(values = pal)
R <- r_all_sliding_21days %>%
mutate(lower_95 = R_lower_95,
upper_95 = R_upper_95,
value = R,
measure = "R",
reference = 1)
r_R <- r_all_sliding_21days %>%
mutate(measure = "r",
value = r,
reference = 0) %>%
bind_rows(R)
r_R_21 <- r_R %>%
ggplot(aes(x = w_max, y = value)) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
geom_line(aes(colour = nhs_region)) +
geom_point(aes(colour = nhs_region)) +
geom_hline(aes(yintercept = reference), linetype = "dashed") +
theme_bw() +
scale_weeks +
theme(legend.position = "bottom",
plot.margin = margin(0.5,1,0,0, "cm"),
strip.background = element_blank(),
strip.placement = "outside"
) +
guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
labs(x = "", y = "") +
scale_colour_manual(values = pal) +
facet_grid(rows = vars(measure),
scales = "free_y",
switch = "y",
labeller = as_labeller(c(r = "Daily growth rate (r)",
R = "Effective reproduction\nnumber (Re)")))And we combine both outputs into a single plot:
ggpubr::ggarrange(r_R_7,
r_R_21,
nrow = 2,
labels = "AUTO",
common.legend = TRUE,
legend = "bottom")
lag_cor_reg <- data.frame()
for (i in 0:30) {
summary <-
all_data %>%
group_by(nhs_region) %>%
mutate(note_lag = lag(count, i)) %>%
## calculate rank correlation and bootstrap CI for each region
group_modify(~getboot(.x)) %>%
mutate(lag = i)
lag_cor_reg <- bind_rows(lag_cor_reg, summary)
}
cor_vs_lag_reg <-
lag_cor_reg %>%
ggplot(aes(lag, r, col = nhs_region)) +
geom_hline(yintercept = 0, lty = "longdash") +
geom_ribbon(aes(ymin = r_low, ymax = r_hi, col = NULL, fill = nhs_region), alpha = 0.2) +
geom_point() +
geom_line() +
facet_wrap(~nhs_region) +
scale_color_manual(values = pal) +
scale_fill_manual(values = pal, guide = F) +
theme_bw() +
labs(x = "Lag between NHS pathways and death data (days)", y = "Pearson's correlation", col = "NHS region") +
theme(legend.position = "bottom") +
guides(color = guide_legend(override.aes = list(fill = NA)))
cor_vs_lag_regWe save the tables created during our analysis:
if (!dir.exists("excel_tables")) {
dir.create("excel_tables")
}
## list all tables, and loop over export
tables_to_export <- c("r_all_sliding", "lag_cor")
for (e in tables_to_export) {
rio::export(get(e),
file.path("excel_tables",
paste0(e, ".xlsx")))
}
## also export result from regression on lagged data
rio::export(lag_mod, file.path("excel_tables", "lag_mod.rds"))The following information documents the system on which the document was compiled.
This provides information on the operating system.
Sys.info()
## sysname
## "Darwin"
## release
## "19.6.0"
## version
## "Darwin Kernel Version 19.6.0: Tue Nov 10 00:10:30 PST 2020; root:xnu-6153.141.10~1/RELEASE_X86_64"
## nodename
## "Mac-1612778809492.local"
## machine
## "x86_64"
## login
## "root"
## user
## "runner"
## effective_user
## "runner"This provides information on the version of R used:
This provides information on the packages used:
sessionInfo()
## R version 4.0.3 (2020-10-10)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.7
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggnewscale_0.4.5 ggpubr_0.4.0 lubridate_1.7.9.2
## [4] chngpt_2020.10-12 cyphr_1.1.0 DT_0.17
## [7] kableExtra_1.3.1 janitor_2.1.0 remotes_2.2.0
## [10] projections_0.5.2 earlyR_0.0.5 epitrix_0.2.2
## [13] distcrete_1.0.3 incidence_1.7.3 rio_0.5.16
## [16] reshape2_1.4.4 rvest_0.3.6 xml2_1.3.2
## [19] linelist_0.0.40.9000 forcats_0.5.1 stringr_1.4.0
## [22] dplyr_1.0.4 purrr_0.3.4 readr_1.4.0
## [25] tidyr_1.1.2 tibble_3.0.6 ggplot2_3.3.3
## [28] tidyverse_1.3.0 here_1.0.1 reportfactory_0.0.5
##
## loaded via a namespace (and not attached):
## [1] minqa_1.2.4 colorspace_2.0-0 selectr_0.4-2 ggsignif_0.6.0
## [5] ellipsis_0.3.1 rprojroot_2.0.2 snakecase_0.11.0 fs_1.5.0
## [9] rstudioapi_0.13 farver_2.0.3 fansi_0.4.2 splines_4.0.3
## [13] knitr_1.31 jsonlite_1.7.2 nloptr_1.2.2.2 broom_0.7.4
## [17] dbplyr_2.1.0 compiler_4.0.3 httr_1.4.2 backports_1.2.1
## [21] assertthat_0.2.1 Matrix_1.2-18 cli_2.3.0 htmltools_0.5.1.1
## [25] tools_4.0.3 gtable_0.3.0 glue_1.4.2 Rcpp_1.0.6
## [29] carData_3.0-4 cellranger_1.1.0 vctrs_0.3.6 nlme_3.1-149
## [33] matchmaker_0.1.1 crosstalk_1.1.1 xfun_0.20 ps_1.5.0
## [37] openxlsx_4.2.3 lme4_1.1-26 lifecycle_0.2.0 statmod_1.4.35
## [41] rstatix_0.6.0 MASS_7.3-53 scales_1.1.1 hms_1.0.0
## [45] parallel_4.0.3 sodium_1.1 yaml_2.2.1 curl_4.3
## [49] gridExtra_2.3 stringi_1.5.3 highr_0.8 kyotil_2020.10-12
## [53] boot_1.3-25 zip_2.1.1 rlang_0.4.10 pkgconfig_2.0.3
## [57] evaluate_0.14 lattice_0.20-41 labeling_0.4.2 htmlwidgets_1.5.3
## [61] cowplot_1.1.1 tidyselect_1.1.0 plyr_1.8.6 magrittr_2.0.1
## [65] R6_2.5.0 generics_0.1.0 DBI_1.1.1 pillar_1.4.7
## [69] haven_2.3.1 foreign_0.8-80 withr_2.4.1 mgcv_1.8-33
## [73] survival_3.2-7 abind_1.4-5 modelr_0.1.8 crayon_1.4.0
## [77] car_3.0-10 utf8_1.1.4 rmarkdown_2.6 viridis_0.5.1
## [81] grid_4.0.3 readxl_1.3.1 data.table_1.13.6 reprex_1.0.0
## [85] digest_0.6.27 webshot_0.5.2 munsell_0.5.0 viridisLite_0.3.0